RouteWeaver: Weaving Mode Selection and Execution into Unified LLM Routing

LLM routing dynamically selects and coordinates heterogeneous language models across diverse tasks.

By Wang, Zhang, Liu +2arXivOct 19, 2026

Score████░░░░░░4.3

Key numbers

11.03% relative improvement over the

Caveats

Preprint; not yet peer reviewed.

VerdictWorth a reader's time today.

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Abstract

LLM routing dynamically selects and coordinates heterogeneous language models across diverse tasks. However, existing approaches are largely built around a predefined execution paradigm, such as single-round, multi-round, or agentic routing, which fixes how model calls are organized and limits adaptation to different query needs. We propose RouteWeaver, a unified routing framework that integrates these routing modes within a single LLM policy, jointly learning which mode to use and how to select models within that mode. To jointly optimize mode selection and within-mode execution, we develop COMET-GRPO (COordinated Mode-Execution Training), which decouples the trajectory-level advantage into separate signals for mode selection and within-mode execution, enabling more targeted credit assignment to each decision level. We further introduce progressive exploration, which gradually shifts training from externally assigned modes to autonomous selection, so that the router first learns how to execute each mode before deciding when to use it. We evaluate RouteWeaver across mathematics, coding, knowledge, logical reasoning, and factual recall. RouteWeaver achieves an average accuracy of 61.4%, an 11.03% relative improvement over the strongest baseline, and outperforms all compared baselines in each of the five domains. Ablations show that both COMET-GRPO and progressive exploration are important for adaptive routing; without progressive exploration, the routing policy collapses entirely to single-round execution.

Xiaohan Wang, Haozhen Zhang, Qingyuan Liu, Tao Feng, Wenya Wang

The editor's rubric

Heuristic review

Leverage4 / 5: A general-purpose tool used across several fields (Adam, ResNet, LoRA, next-generation sequencing).; 24% of this desk’s rubric
Magnitude3 / 5: Large gain: roughly 2x, or a clear new state of the art on a hard, unsaturated problem.; 18% of this desk’s rubric
Evidence3 / 5: Solid: multiple benchmarks or cohorts, ablations, fair baselines, released code or data.; 14% of this desk’s rubric
Novelty2 / 5: A new combination of known ideas.; 16% of this desk’s rubric
Trajectory2 / 5: Some room to improve with obvious engineering.; 18% of this desk’s rubric
Stakes2 / 5: Benefits a professional community (practitioners, clinicians, engineers).; 10% of this desk’s rubric

Dimension	Level	Weight	What that level means
Leverage	████░ 4	24%	A general-purpose tool used across several fields (Adam, ResNet, LoRA, next-generation sequencing).
Magnitude	███░░ 3	18%	Large gain: roughly 2x, or a clear new state of the art on a hard, unsaturated problem.
Evidence	███░░ 3	14%	Solid: multiple benchmarks or cohorts, ablations, fair baselines, released code or data.
Novelty	██░░░ 2	16%	A new combination of known ideas.
Trajectory	██░░░ 2	18%	Some room to improve with obvious engineering.
Stakes	██░░░ 2	10%	Benefits a professional community (practitioners, clinicians, engineers).

Editor’s rationale

Heuristic triage from title and abstract text only, not a reading of the paper. Cues found: method (we propose, new method); breadth (many tasks); gains (absolute improvement, outperforms); verification (ablations); stakes (general AI).

How the score was computed

rank-2026-10-18

Score████░░░░░░4.3

Score = 10 × (65% × adjusted merit / 10 + 25% × attention + 10% × freshness)

Desks place stronger verdicts first, then use this score to order work within the same verdict.

Merit: 5.6 / 10; Weighted rubric, evidence-gated.
Adjusted merit: 4.7 / 10; Shrunk toward the desk prior by editor confidence (44%).
Attention: 36%; Citations, upvotes, points, mentions.
Freshness: 28%; Half-life decay since publication.

Citations0 (reference 15, via semantic-scholar, Oct 23, 2026, 13:24 UTC)
Influential citations0 (reference 3, via semantic-scholar, Oct 23, 2026, 13:24 UTC)
Hugging Face upvotes13 (reference 25, via hf-daily, Oct 23, 2026, 13:24 UTC)
GitHub stars0 (reference 250, via hf-daily, Oct 23, 2026, 13:24 UTC)

The record

Reviewed by heuristic-v7 on Oct 23, 2026, 13:24 UTC. Paper type: method.
BRIEF, No.8 in the Artificial Intelligence edition of October 23, 2026.